Collaborative Image Triage With Humans And Computer Vision
2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)(2016)
摘要
As the technology for acquiring and storing images becomes more prevalent, we are faced with a growing need to sort and label these images. At this time, computer vision algorithms cannot parse abstract concepts from images like a human. As a result, there may be performance gains possible from the integration of human analysts with computer vision agents. We present an image triage system which facilitates the collaboration of heterogeneous agents through a novel unsupervised meta-learning technique. The system iteratively allocates images for binary classification among heterogeneous agents according to the Generalized Assignment Problem (GAP) and combines the classification results using the Spectral Meta-Learner (SML). In simulation, we demonstrate that the proposed system achieves significant speed-up over a naive parallel assignment strategy without sacrificing accuracy.
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关键词
collaborative image triage,computer vision algorithms,human analysts,image triage system,heterogeneous agents,unsupervised meta-learning technique,binary classification,generalized assignment problem,GAP,spectral meta-learner,SML,naive parallel assignment strategy
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